This series of files compile all analyses done during Chapter 3:
- Section 1 presents the calculation of the indices of exposure.
- Section 2 presents variable exploration and regressions results.
- Section 3 presents species distribution models.
All analyses have been done with R 4.0.2.
Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it
⏪ | 🏠 | ⏩
Sources of activity considered for the analyses:
- aquaculture: mussel farm (AquaInf)
- city: general diffusive influence, wharves (CityInf, CityWha)
- industry: general diffusive influence, wharves (Indu, InduWha)
- sediment dredging: collection zones, dumping zones (DredColl, DredDump)
- commercial shipping: mooring sites, traffic routes (ShipMoor, ShipTraf)
- sewers: rainwater drains, wastewater drains (SewRain, SewWast)
Fisheries data considered for the analyses (expressed as number of fishing events or kilograms of collected individuals for each gear):
| Dredge |
FishDred |
2010-2014 |
21 |
Mactromeris polynyma |
| Net |
FishNet |
2010 |
5 |
Clupea harengus, Gadus morhua |
| Trap |
FishTrap |
2010-2015 |
1061 |
Buccinum sp., Cancer irroratus, Chionoecetes opilio, Homarus americanus |
| Bottom-trawl |
FishTraw |
2013-2014 |
2 |
Pandalus borealis |
1. Spatial variation of exposure indices
Here, we compute semivariograms for each exposure index (on the whole raster, not only extracted values at the stations).
Aquaculture
## Model selected: Sph
## nugget = 0; sill = 0.00742; range = 6.06524; kappa = 0.5

City
## Model selected: Lin
## nugget = 0; sill = 0.0163; range = 8.41153; kappa = 0.5

Sediment dredging
## Model selected: Exp
## nugget = 0.00068; sill = 0.01557; range = 2.67375; kappa = 0.5

Industry
## Model selected: Lin
## nugget = 0; sill = 0.01814; range = 6.79328; kappa = 0.5

Sewers
## Model selected: Sph
## nugget = 0; sill = 0.01603; range = 11.91271; kappa = 0.5

Shipping
## Model selected: Lin
## nugget = 0; sill = 0.06999; range = 4.40942; kappa = 0.5

Fisheries: Dredge
## Model selected: Lin
## nugget = 0; sill = 0.01019; range = 2.81568; kappa = 0.5

Fisheries: Net
## Model selected: Exp
## nugget = 2e-05; sill = 0.00456; range = 0.70613; kappa = 0.5

Fisheries: Trap
## Model selected: Lin
## nugget = 0.00034; sill = 0.00128; range = 1.12045; kappa = 0.5

Fisheries: Bottom-trawling
## Model selected: Lin
## nugget = 0; sill = 0.03509; range = 3.90932; kappa = 0.5

2. Relationships between exposure indices and abiotic parameters
2.1. Covariation
Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.
⚠️ Only linear models were implemented for now, as there are some bugs with the calculation of the others.
Aquaculture

City

Sediment dredging

Industry

Sewers

Shipping

Fisheries: Dredge

Fisheries: Net

Fisheries: Trap

Fisheries: Bottom-trawling

Cumulative exposure

2.2. Correlation
Correlations have been calculated with Spearman’s rank coefficient.
Correlation coefficients between exposure indices and ecosystem variables
| aquaculture |
-0.326 |
0.145 |
0.363 |
-0.343 |
-0.108 |
-0.621 |
-0.693 |
-0.71 |
-0.696 |
-0.599 |
-0.74 |
-0.716 |
-0.698 |
-0.715 |
0.373 |
0.025 |
-0.029 |
0.42 |
0.22 |
| city |
-0.151 |
-0.072 |
0.415 |
-0.257 |
-0.12 |
-0.263 |
-0.153 |
-0.186 |
0.059 |
-0.031 |
-0.173 |
-0.234 |
-0.182 |
-0.035 |
-0.092 |
-0.008 |
-0.15 |
-0.049 |
0.024 |
| dredging |
0.303 |
-0.122 |
-0.103 |
0.118 |
0.04 |
0.211 |
0.14 |
0.377 |
0.548 |
0.616 |
0.526 |
0.185 |
0.271 |
0.442 |
-0.156 |
-0.124 |
0.03 |
-0.048 |
0.016 |
| industry |
0.172 |
-0.119 |
0.004 |
0.031 |
0.059 |
0.116 |
0.047 |
0.302 |
0.475 |
0.556 |
0.479 |
0.085 |
0.178 |
0.348 |
-0.191 |
-0.111 |
0.032 |
-0.101 |
-0.009 |
| sewers |
0.257 |
-0.034 |
-0.346 |
0.292 |
0.254 |
0.616 |
0.588 |
0.671 |
0.68 |
0.607 |
0.733 |
0.589 |
0.687 |
0.681 |
-0.352 |
-0.058 |
0.061 |
-0.387 |
-0.194 |
| shipping |
0.46 |
-0.251 |
-0.299 |
0.315 |
-0.034 |
0.526 |
0.473 |
0.604 |
0.677 |
0.675 |
0.697 |
0.53 |
0.55 |
0.668 |
-0.169 |
-0.067 |
0.039 |
-0.146 |
-0.073 |
| fisheries_dredge |
-0.238 |
0.068 |
0.246 |
-0.241 |
-0.045 |
-0.465 |
-0.458 |
-0.558 |
-0.602 |
-0.648 |
-0.649 |
-0.42 |
-0.474 |
-0.578 |
0.334 |
0.028 |
-0.084 |
0.423 |
0.228 |
| fisheries_net |
0.004 |
-0.055 |
-0.158 |
0.119 |
0.191 |
0.078 |
-0.001 |
0.055 |
0.033 |
0.055 |
0.106 |
0.01 |
0.036 |
0.026 |
-0.112 |
-0.137 |
0.069 |
-0.035 |
0.127 |
| fisheries_trap |
-0.503 |
0.158 |
0.422 |
-0.38 |
-0.095 |
-0.444 |
-0.346 |
-0.323 |
-0.318 |
-0.291 |
-0.301 |
-0.353 |
-0.376 |
-0.358 |
0.077 |
0.182 |
-0.032 |
-0.062 |
-0.169 |
| fisheries_trawl |
-0.215 |
0.172 |
0.088 |
-0.182 |
-0.105 |
-0.237 |
-0.306 |
-0.349 |
-0.451 |
-0.368 |
-0.466 |
-0.313 |
-0.308 |
-0.397 |
0.216 |
-0.009 |
-0.038 |
0.162 |
0.032 |
| cumulative_exposure |
0.254 |
-0.097 |
-0.116 |
0.142 |
0.086 |
0.267 |
0.153 |
0.327 |
0.46 |
0.491 |
0.425 |
0.194 |
0.334 |
0.409 |
-0.072 |
-0.054 |
0.019 |
-0.085 |
-0.105 |
p-values of correlation test between exposure indices and ecosystem variables
| aquaculture |
0.0005798 |
0.1356 |
0.0001144 |
0.0002802 |
0.2665 |
7.874e-13 |
1.019e-16 |
8.293e-18 |
6.586e-17 |
7.689e-12 |
5.399e-20 |
2.963e-18 |
4.954e-17 |
3.467e-18 |
7.175e-05 |
0.7968 |
0.7684 |
6.03e-06 |
0.0219 |
| city |
0.1198 |
0.4579 |
8.144e-06 |
0.007226 |
0.215 |
0.005946 |
0.1137 |
0.05456 |
0.5448 |
0.7521 |
0.0742 |
0.01499 |
0.05964 |
0.7218 |
0.3434 |
0.9372 |
0.121 |
0.615 |
0.8039 |
| dredging |
0.001441 |
0.2084 |
0.2889 |
0.2244 |
0.6842 |
0.02837 |
0.1496 |
5.871e-05 |
8.644e-10 |
1.262e-12 |
5.121e-09 |
0.05496 |
0.004503 |
1.7e-06 |
0.1066 |
0.2012 |
0.7541 |
0.6201 |
0.8731 |
| industry |
0.07517 |
0.2204 |
0.9682 |
0.7525 |
0.5423 |
0.2336 |
0.6287 |
0.001477 |
2.093e-07 |
4.285e-10 |
1.622e-07 |
0.383 |
0.06579 |
0.00022 |
0.0474 |
0.2517 |
0.7427 |
0.3003 |
0.9224 |
| sewers |
0.007329 |
0.7298 |
0.0002498 |
0.002145 |
0.007908 |
1.268e-12 |
2.16e-11 |
1.947e-15 |
6.062e-16 |
3.165e-12 |
2.031e-19 |
2.002e-11 |
2.366e-16 |
5.388e-16 |
0.0001859 |
0.5543 |
0.5333 |
3.587e-05 |
0.04472 |
| shipping |
5.587e-07 |
0.008923 |
0.001653 |
0.0009122 |
0.7234 |
4.965e-09 |
2.295e-07 |
4.656e-12 |
9.206e-16 |
1.171e-15 |
5.419e-17 |
3.563e-09 |
6.737e-10 |
2.923e-15 |
0.08053 |
0.4896 |
0.6886 |
0.1309 |
0.4528 |
| fisheries_dredge |
0.01314 |
0.4825 |
0.01028 |
0.01193 |
0.6404 |
3.925e-07 |
6.196e-07 |
3.496e-10 |
5.797e-12 |
3.49e-14 |
2.894e-14 |
6.176e-06 |
2.193e-07 |
5.928e-11 |
0.0004168 |
0.7711 |
0.3857 |
5.068e-06 |
0.01743 |
| fisheries_net |
0.9713 |
0.5721 |
0.1025 |
0.2201 |
0.04787 |
0.4215 |
0.9885 |
0.573 |
0.7361 |
0.5728 |
0.2767 |
0.9196 |
0.7104 |
0.7874 |
0.2496 |
0.1576 |
0.4781 |
0.7212 |
0.1906 |
| fisheries_trap |
2.878e-08 |
0.1014 |
5.265e-06 |
4.889e-05 |
0.3305 |
1.481e-06 |
0.0002478 |
0.0006488 |
0.0008039 |
0.002278 |
0.001548 |
0.0001765 |
6.138e-05 |
0.0001419 |
0.4277 |
0.05927 |
0.7393 |
0.524 |
0.0798 |
| fisheries_trawl |
0.02573 |
0.07593 |
0.3644 |
0.05997 |
0.2811 |
0.0134 |
0.001257 |
0.0002149 |
9.741e-07 |
8.969e-05 |
3.712e-07 |
0.0009717 |
0.001194 |
2.129e-05 |
0.0248 |
0.9286 |
0.6962 |
0.09349 |
0.7425 |
| cumulative_exposure |
0.007953 |
0.3202 |
0.2331 |
0.1423 |
0.3777 |
0.005263 |
0.1132 |
0.0005613 |
5.53e-07 |
6.626e-08 |
4.58e-06 |
0.04408 |
0.0004186 |
1.092e-05 |
0.4576 |
0.5811 |
0.8475 |
0.3817 |
0.2795 |

3. Species abundances by cumulative exposure index
The following graphs present the distribution of sampled phyla along a gradient of cumulative exposure.

The threshold classification is based on the exposure index: the higher the index, the lower the status.
Phylum mean abundances by group
| Annelida |
12.5 |
27 |
27.5 |
42.1 |
21.6 |
| Arthropoda |
10.8 |
17 |
42 |
66.3 |
29.4 |
| Cnidaria |
0 |
0 |
0 |
0 |
0.0303 |
| Echinodermata |
0.25 |
0 |
2.59 |
2.68 |
4.55 |
| Mollusca |
13.8 |
5.6 |
9.34 |
20.7 |
10.7 |
| Nematoda |
0 |
0.2 |
1.72 |
14.6 |
13.7 |
| Nemertea |
0 |
0 |
0.138 |
0.324 |
0 |
| Sipuncula |
0.5 |
0 |
0.483 |
0.162 |
0.212 |

4. Regressions between exposure indices and community characteristics
4.1. Data manipulation
For the following analyses, independant variables are exposure indices, dependant variables are community characteristics. Variables have been standardized by mean and standard-deviation.
All stations and predictors were selected for the regressions, as we are interested in each of them (following graphs are for information only).

Correlation coefficients between exposure indices
| aquaculture |
1 |
-0.101 |
-0.39 |
-0.374 |
-0.852 |
-0.669 |
0.792 |
-0.111 |
0.202 |
0.62 |
| city |
-0.101 |
1 |
0.269 |
0.248 |
0.003 |
0.165 |
-0.292 |
-0.072 |
-0.022 |
-0.355 |
| dredging |
-0.39 |
0.269 |
1 |
0.933 |
0.542 |
0.649 |
-0.598 |
0.005 |
-0.117 |
-0.479 |
| industry |
-0.374 |
0.248 |
0.933 |
1 |
0.555 |
0.537 |
-0.544 |
0.082 |
0.044 |
-0.525 |
| sewers |
-0.852 |
0.003 |
0.542 |
0.555 |
1 |
0.607 |
-0.745 |
0.182 |
-0.148 |
-0.556 |
| shipping |
-0.669 |
0.165 |
0.649 |
0.537 |
0.607 |
1 |
-0.663 |
0.037 |
-0.373 |
-0.619 |
| fisheries_dredge |
0.792 |
-0.292 |
-0.598 |
-0.544 |
-0.745 |
-0.663 |
1 |
-0.08 |
0.142 |
0.514 |
| fisheries_net |
-0.111 |
-0.072 |
0.005 |
0.082 |
0.182 |
0.037 |
-0.08 |
1 |
0.135 |
-0.071 |
| fisheries_trap |
0.202 |
-0.022 |
-0.117 |
0.044 |
-0.148 |
-0.373 |
0.142 |
0.135 |
1 |
0.23 |
| fisheries_trawl |
0.62 |
-0.355 |
-0.479 |
-0.525 |
-0.556 |
-0.619 |
0.514 |
-0.071 |
0.23 |
1 |

4.2. Univariate regressions
We used linear models for the regressions on community characteristics. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models).
We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the table below:
| Aquaculture |
|
|
|
|
|
| City |
|
|
|
|
|
| Sediment dredging |
|
- |
|
|
+ |
| Industry |
|
|
|
|
|
| Sewers |
- |
|
|
- |
- |
| Shipping |
|
|
|
|
|
| Fisheries: Dredge |
+ |
|
|
+ |
|
| Fisheries: Net |
|
|
|
|
|
| Fisheries: Trap |
|
|
|
|
|
| Fisheries: Bottom-trawling |
|
|
|
|
- |
| Adjusted \(R^{2}\) |
0.17 |
0.01 |
0 |
0.13 |
0.06 |
Details of the regressions, with diagnostics and cross-validation, are summarized below.
Richness
## FULL MODEL
## Adjusted R2 is: 0.15
Fitting linear model: S ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
| (Intercept) |
-5.499e-16 |
0.08891 |
-6.185e-15 |
1 |
|
| aquaculture |
0.07318 |
0.1099 |
0.6657 |
0.5072 |
|
| city |
-0.07626 |
0.1149 |
-0.664 |
0.5083 |
|
| dredging |
-0.01389 |
0.1115 |
-0.1245 |
0.9011 |
|
| industry |
-0.06323 |
0.1368 |
-0.4624 |
0.6449 |
|
| sewers |
-0.219 |
0.1313 |
-1.668 |
0.09845 |
|
| shipping |
0.1356 |
0.1018 |
1.332 |
0.186 |
|
| fisheries_dredge |
0.2492 |
0.1002 |
2.488 |
0.01455 |
* |
| fisheries_net |
-0.0005094 |
0.08985 |
-0.00567 |
0.9955 |
|
| fisheries_trap |
0.05792 |
0.1022 |
0.5667 |
0.5722 |
|
| fisheries_trawl |
0.1316 |
0.09454 |
1.392 |
0.1672 |
|
## RMSE from cross-validation: 46.27009
Variance Inflation Factors
| VIF |
1.23 |
1.29 |
1.25 |
1.53 |
1.47 |
1.14 |
1.12 |
1.01 |
1.14 |
1.06 |

## REDUCED MODEL
## Adjusted R2 is: 0.17
Fitting linear model: S ~ sewers + fisheries_dredge
| (Intercept) |
-6.547e-16 |
0.08778 |
-7.459e-15 |
1 |
|
| sewers |
-0.2817 |
0.09165 |
-3.073 |
0.002698 |
* * |
| fisheries_dredge |
0.2548 |
0.09165 |
2.78 |
0.006435 |
* * |
## RMSE from cross-validation: 0.9211796
Variance Inflation Factors
| VIF |
1.04 |
1.04 |

Density
## FULL MODEL
## Adjusted R2 is: -0.03
Fitting linear model: N ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
| (Intercept) |
4.246e-16 |
0.09784 |
4.339e-15 |
1 |
|
| aquaculture |
-0.06837 |
0.121 |
-0.5652 |
0.5733 |
|
| city |
0.1147 |
0.1264 |
0.9073 |
0.3665 |
|
| dredging |
-0.1187 |
0.1227 |
-0.9673 |
0.3358 |
|
| industry |
-0.1857 |
0.1505 |
-1.234 |
0.2202 |
|
| sewers |
0.1758 |
0.1445 |
1.217 |
0.2265 |
|
| shipping |
-0.1097 |
0.112 |
-0.979 |
0.33 |
|
| fisheries_dredge |
0.02122 |
0.1102 |
0.1925 |
0.8478 |
|
| fisheries_net |
-0.04556 |
0.09888 |
-0.4608 |
0.646 |
|
| fisheries_trap |
0.01836 |
0.1125 |
0.1632 |
0.8707 |
|
| fisheries_trawl |
0.03962 |
0.104 |
0.3808 |
0.7042 |
|
## RMSE from cross-validation: 66.15888
Variance Inflation Factors
| VIF |
1.23 |
1.29 |
1.25 |
1.53 |
1.47 |
1.14 |
1.12 |
1.01 |
1.14 |
1.06 |

## REDUCED MODEL
## Adjusted R2 is: 0.01
Fitting linear model: N ~ dredging
| (Intercept) |
1.931e-16 |
0.09561 |
2.019e-15 |
1 |
|
| dredging |
-0.148 |
0.09606 |
-1.541 |
0.1264 |
|
## RMSE from cross-validation: 1.007331
Variance Inflation Factors
| VIF |
1 |

Biomass
## FULL MODEL
## Adjusted R2 is: -0.05
Fitting linear model: B ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
| (Intercept) |
-2.779e-16 |
0.09871 |
-2.816e-15 |
1 |
|
| aquaculture |
-0.1066 |
0.122 |
-0.8731 |
0.3847 |
|
| city |
-0.1521 |
0.1275 |
-1.193 |
0.2357 |
|
| dredging |
0.02227 |
0.1238 |
0.1799 |
0.8576 |
|
| industry |
0.1062 |
0.1518 |
0.6997 |
0.4858 |
|
| sewers |
-0.2088 |
0.1458 |
-1.433 |
0.1552 |
|
| shipping |
-0.137 |
0.113 |
-1.212 |
0.2284 |
|
| fisheries_dredge |
-0.06607 |
0.1112 |
-0.594 |
0.5539 |
|
| fisheries_net |
-0.007141 |
0.09976 |
-0.07159 |
0.9431 |
|
| fisheries_trap |
0.01514 |
0.1135 |
0.1334 |
0.8941 |
|
| fisheries_trawl |
0.003222 |
0.105 |
0.0307 |
0.9756 |
|
## RMSE from cross-validation: 8.265183
Variance Inflation Factors
| VIF |
1.23 |
1.29 |
1.25 |
1.53 |
1.47 |
1.14 |
1.12 |
1.01 |
1.14 |
1.06 |

## REDUCED MODEL
## Adjusted R2 is: 0
Fitting linear model: B ~ 1
| (Intercept) |
-3.205e-17 |
0.09623 |
-3.331e-16 |
1 |
|
## RMSE from cross-validation: 0.9988509
Quitting from lines 403-405 (C3_analyses_B.Rmd) Error in h(simpleError(msg, call)) : erreur d’évaluation de l’argument ‘x’ lors de la sélection d’une méthode pour la fonction ‘t’ : erreur d’évaluation de l’argument ‘x’ lors de la sélection d’une méthode pour la fonction ‘as.data.frame’ : indice hors limites De plus : There were 50 or more warnings (use warnings() to see the first 50)
Diversity
## FULL MODEL
## Adjusted R2 is: 0.11
Fitting linear model: H ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
| (Intercept) |
-7.785e-17 |
0.09099 |
-8.556e-16 |
1 |
|
| aquaculture |
0.08597 |
0.1125 |
0.7642 |
0.4466 |
|
| city |
-0.1044 |
0.1175 |
-0.8879 |
0.3768 |
|
| dredging |
0.1533 |
0.1141 |
1.343 |
0.1825 |
|
| industry |
-0.01545 |
0.14 |
-0.1104 |
0.9124 |
|
| sewers |
-0.3325 |
0.1344 |
-2.475 |
0.01506 |
* |
| shipping |
0.101 |
0.1042 |
0.9694 |
0.3348 |
|
| fisheries_dredge |
0.1792 |
0.1025 |
1.748 |
0.08365 |
|
| fisheries_net |
0.05076 |
0.09196 |
0.5521 |
0.5822 |
|
| fisheries_trap |
0.01163 |
0.1046 |
0.1112 |
0.9117 |
|
| fisheries_trawl |
-0.03955 |
0.09676 |
-0.4087 |
0.6836 |
|
## RMSE from cross-validation: 16.43584
Variance Inflation Factors
| VIF |
1.23 |
1.29 |
1.25 |
1.53 |
1.47 |
1.14 |
1.12 |
1.01 |
1.14 |
1.06 |

## REDUCED MODEL
## Adjusted R2 is: 0.13
Fitting linear model: H ~ sewers + fisheries_dredge
| (Intercept) |
-3.89e-17 |
0.08971 |
-4.337e-16 |
1 |
|
| sewers |
-0.2805 |
0.09366 |
-2.995 |
0.003424 |
* * |
| fisheries_dredge |
0.1963 |
0.09366 |
2.096 |
0.03851 |
* |
## RMSE from cross-validation: 0.934956
Variance Inflation Factors
| VIF |
1.04 |
1.04 |

Evenness
## FULL MODEL
## Adjusted R2 is: 0.01
Fitting linear model: J ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
| (Intercept) |
-2.727e-16 |
0.09587 |
-2.845e-15 |
1 |
|
| aquaculture |
0.02333 |
0.1185 |
0.1968 |
0.8444 |
|
| city |
-0.06275 |
0.1239 |
-0.5067 |
0.6135 |
|
| dredging |
0.1953 |
0.1202 |
1.624 |
0.1076 |
|
| industry |
-0.002176 |
0.1475 |
-0.01475 |
0.9883 |
|
| sewers |
-0.2688 |
0.1416 |
-1.899 |
0.06058 |
|
| shipping |
-0.009191 |
0.1097 |
-0.08374 |
0.9334 |
|
| fisheries_dredge |
0.05474 |
0.108 |
0.5068 |
0.6134 |
|
| fisheries_net |
0.04791 |
0.09688 |
0.4945 |
0.6221 |
|
| fisheries_trap |
-0.04752 |
0.1102 |
-0.4312 |
0.6673 |
|
| fisheries_trawl |
-0.15 |
0.1019 |
-1.472 |
0.1444 |
|
## RMSE from cross-validation: 91.35394
Variance Inflation Factors
| VIF |
1.23 |
1.29 |
1.25 |
1.53 |
1.47 |
1.14 |
1.12 |
1.01 |
1.14 |
1.06 |

## REDUCED MODEL
## Adjusted R2 is: 0.06
Fitting linear model: J ~ dredging + sewers + fisheries_trawl
| (Intercept) |
-2.606e-16 |
0.09342 |
-2.79e-15 |
1 |
|
| dredging |
0.1638 |
0.09907 |
1.654 |
0.1012 |
|
| sewers |
-0.2737 |
0.1003 |
-2.728 |
0.007475 |
* * |
| fisheries_trawl |
-0.1422 |
0.09579 |
-1.484 |
0.1408 |
|
## RMSE from cross-validation: 1.026905
Variance Inflation Factors
| VIF |
1.06 |
1.07 |
1.02 |

Annelids
## FULL MODEL
## McFadden's pseudo-R2 is: 0.09
Fitting generalized (poisson/log) linear model: annelids ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
| (Intercept) |
3.345 |
0.0188 |
177.9 |
0 |
* * * |
| aquaculture |
0.05994 |
0.02136 |
2.806 |
0.005015 |
* * |
| city |
0.09822 |
0.02252 |
4.361 |
1.292e-05 |
* * * |
| dredging |
-0.1569 |
0.02993 |
-5.243 |
1.582e-07 |
* * * |
| industry |
-0.2086 |
0.03425 |
-6.089 |
1.136e-09 |
* * * |
| sewers |
0.08524 |
0.02907 |
2.932 |
0.003363 |
* * |
| shipping |
0.05828 |
0.01853 |
3.145 |
0.001658 |
* * |
| fisheries_dredge |
-0.08875 |
0.02583 |
-3.436 |
0.0005914 |
* * * |
| fisheries_net |
-0.0613 |
0.02287 |
-2.681 |
0.007347 |
* * |
| fisheries_trap |
0.08082 |
0.0171 |
4.725 |
2.297e-06 |
* * * |
| fisheries_trawl |
-0.2465 |
0.03327 |
-7.41 |
1.264e-13 |
* * * |
## Unbiased RMSE from cross-validation: 36.48564
Variance Inflation Factors
| VIF |
1.3 |
1.5 |
1.25 |
1.55 |
1.44 |
1.13 |
1.19 |
1 |
1.37 |
1.04 |

## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.09
Fitting generalized (poisson/log) linear model: annelids ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
| (Intercept) |
3.345 |
0.0188 |
177.9 |
0 |
* * * |
| aquaculture |
0.05994 |
0.02136 |
2.806 |
0.005015 |
* * |
| city |
0.09822 |
0.02252 |
4.361 |
1.292e-05 |
* * * |
| dredging |
-0.1569 |
0.02993 |
-5.243 |
1.582e-07 |
* * * |
| industry |
-0.2086 |
0.03425 |
-6.089 |
1.136e-09 |
* * * |
| sewers |
0.08524 |
0.02907 |
2.932 |
0.003363 |
* * |
| shipping |
0.05828 |
0.01853 |
3.145 |
0.001658 |
* * |
| fisheries_dredge |
-0.08875 |
0.02583 |
-3.436 |
0.0005914 |
* * * |
| fisheries_net |
-0.0613 |
0.02287 |
-2.681 |
0.007347 |
* * |
| fisheries_trap |
0.08082 |
0.0171 |
4.725 |
2.297e-06 |
* * * |
| fisheries_trawl |
-0.2465 |
0.03327 |
-7.41 |
1.264e-13 |
* * * |
## Unbiased RMSE from cross-validation: 36.51031
Variance Inflation Factors
| VIF |
1.3 |
1.5 |
1.25 |
1.55 |
1.44 |
1.13 |
1.19 |
1 |
1.37 |
1.04 |

Arthropods
## FULL MODEL
## McFadden's pseudo-R2 is: 0.17
Fitting generalized (poisson/log) linear model: arthropods ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
| (Intercept) |
3.632 |
0.01686 |
215.4 |
0 |
* * * |
| aquaculture |
-0.2642 |
0.02517 |
-10.5 |
8.981e-26 |
* * * |
| city |
0.2199 |
0.01938 |
11.35 |
7.596e-30 |
* * * |
| dredging |
-0.2205 |
0.0253 |
-8.715 |
2.902e-18 |
* * * |
| industry |
-0.5373 |
0.02999 |
-17.92 |
8.988e-72 |
* * * |
| sewers |
0.5954 |
0.02366 |
25.16 |
1.05e-139 |
* * * |
| shipping |
-0.09925 |
0.01618 |
-6.133 |
8.65e-10 |
* * * |
| fisheries_dredge |
0.08097 |
0.01391 |
5.821 |
5.845e-09 |
* * * |
| fisheries_net |
-0.09027 |
0.02028 |
-4.451 |
8.549e-06 |
* * * |
| fisheries_trap |
-0.08966 |
0.01719 |
-5.216 |
1.829e-07 |
* * * |
| fisheries_trawl |
0.01148 |
0.01623 |
0.7075 |
0.4793 |
|
## Unbiased RMSE from cross-validation: 95.792
Variance Inflation Factors
| VIF |
1.17 |
1.33 |
1.18 |
1.82 |
1.81 |
1.1 |
1.09 |
1 |
1.22 |
1.06 |

## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.17
Fitting generalized (poisson/log) linear model: arthropods ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap
| (Intercept) |
3.632 |
0.01685 |
215.6 |
0 |
* * * |
| aquaculture |
-0.2646 |
0.02512 |
-10.53 |
6.142e-26 |
* * * |
| city |
0.2172 |
0.019 |
11.43 |
2.817e-30 |
* * * |
| dredging |
-0.2203 |
0.0253 |
-8.705 |
3.175e-18 |
* * * |
| industry |
-0.5362 |
0.02993 |
-17.91 |
9.222e-72 |
* * * |
| sewers |
0.5923 |
0.02321 |
25.51 |
1.388e-143 |
* * * |
| shipping |
-0.1014 |
0.0159 |
-6.375 |
1.833e-10 |
* * * |
| fisheries_dredge |
0.07976 |
0.01381 |
5.775 |
7.703e-09 |
* * * |
| fisheries_net |
-0.09055 |
0.02028 |
-4.466 |
7.976e-06 |
* * * |
| fisheries_trap |
-0.08914 |
0.01717 |
-5.191 |
2.097e-07 |
* * * |
## Unbiased RMSE from cross-validation: 89.3128
Variance Inflation Factors
| VIF |
1.16 |
1.3 |
1.18 |
1.81 |
1.78 |
1.08 |
1.08 |
1 |
1.22 |

Molluscs
## FULL MODEL
## McFadden's pseudo-R2 is: 0.19
Fitting generalized (poisson/log) linear model: molluscs ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap + fisheries_trawl
| (Intercept) |
2.459 |
0.03058 |
80.41 |
0 |
* * * |
| aquaculture |
0.06516 |
0.02674 |
2.436 |
0.01484 |
* |
| city |
0.1413 |
0.032 |
4.415 |
1.009e-05 |
* * * |
| dredging |
-0.0954 |
0.04178 |
-2.284 |
0.0224 |
* |
| industry |
0.3211 |
0.03663 |
8.766 |
1.857e-18 |
* * * |
| sewers |
-0.3914 |
0.04285 |
-9.135 |
6.562e-20 |
* * * |
| shipping |
-0.2981 |
0.04308 |
-6.919 |
4.539e-12 |
* * * |
| fisheries_dredge |
0.1036 |
0.01878 |
5.518 |
3.432e-08 |
* * * |
| fisheries_net |
0.06904 |
0.02534 |
2.724 |
0.006446 |
* * |
| fisheries_trap |
0.04718 |
0.02484 |
1.899 |
0.05756 |
|
| fisheries_trawl |
0.01166 |
0.02586 |
0.4508 |
0.6521 |
|
## Unbiased RMSE from cross-validation: 17.50708
Variance Inflation Factors
| VIF |
1.22 |
1.57 |
1.51 |
1.54 |
1.26 |
1.19 |
1.09 |
1.01 |
1.34 |
1.06 |

## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.17
Fitting generalized (poisson/log) linear model: arthropods ~ aquaculture + city + dredging + industry + sewers + shipping + fisheries_dredge + fisheries_net + fisheries_trap
| (Intercept) |
3.632 |
0.01685 |
215.6 |
0 |
* * * |
| aquaculture |
-0.2646 |
0.02512 |
-10.53 |
6.142e-26 |
* * * |
| city |
0.2172 |
0.019 |
11.43 |
2.817e-30 |
* * * |
| dredging |
-0.2203 |
0.0253 |
-8.705 |
3.175e-18 |
* * * |
| industry |
-0.5362 |
0.02993 |
-17.91 |
9.222e-72 |
* * * |
| sewers |
0.5923 |
0.02321 |
25.51 |
1.388e-143 |
* * * |
| shipping |
-0.1014 |
0.0159 |
-6.375 |
1.833e-10 |
* * * |
| fisheries_dredge |
0.07976 |
0.01381 |
5.775 |
7.703e-09 |
* * * |
| fisheries_net |
-0.09055 |
0.02028 |
-4.466 |
7.976e-06 |
* * * |
| fisheries_trap |
-0.08914 |
0.01717 |
-5.191 |
2.097e-07 |
* * * |
## Unbiased RMSE from cross-validation: 91.1573
Variance Inflation Factors
| VIF |
1.16 |
1.3 |
1.18 |
1.81 |
1.78 |
1.08 |
1.08 |
1 |
1.22 |

🔝